Knowledge Graph Error Detection with Contrastive Confidence Adaption
Authors: Xiangyu Liu, Yang Liu, Wei Hu
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that CCA outperforms state-of-the-art baselines, especially in detecting semantically-similar noise and adversarial noise. |
| Researcher Affiliation | Academia | 1 State Key Laboratory for Novel Software Technology, Nanjing University, China 2 National Institute of Healthcare Data Science, Nanjing University, China {xyl, yliu20}.nju@gmail.com, whu@nju.edu.cn |
| Pseudocode | No | The paper describes the model architecture and components but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Datasets and source code are available at https://github.com/nju-websoft/CCA. |
| Open Datasets | Yes | We conduct our experiments on FB15K-237 (Toutanova et al. 2015) and WN18RR (Dettmers et al. 2018). |
| Dataset Splits | No | The paper discusses training and testing, and mentions 'We randomly divide it into training and testing sets, Dtrain and Dtest' for adversarial noise generation, but does not specify validation splits for the main experiments or general train/test/validation splits. |
| Hardware Specification | Yes | All experiments are conducted on two Intel Xeon Gold 6326 CPUs, 512GB RAM, and one NVIDIA RTX A6000 GPU. |
| Software Dependencies | No | We leverage the BERT-base model from huggingface as the PLM. We use Py Torch to implement our model and employ the Adam W optimizer and a cosine decay scheduler with a linear warm-up for optimization. (No specific version numbers are provided for PyTorch or huggingface modules). |
| Experiment Setup | No | The grid search is used for hyperparameter tuning. (No specific hyperparameter values like learning rate, batch size, or number of epochs are explicitly stated for the final model setup in the main text). |